journal article·2022-10

Estimation of Realistic Microtexture Region Orientation Distribution Functions Using Eddy Current Data

Laura Homa, Matthew Cherry, John Wertz

JOM· Volume 74· Issue 10
Publisher page

Abstract

Microtexture regions (MTRs) within titanium alloys are collections of grains with similar crystallographic orientation. The presence of MTRs can be detrimental to the life of an engine component; thus, a method for detecting and characterizing MTR is needed. Eddy current testing, a nondestructive evaluation method, is sensitive to changes in conductivity which are related to local changes in crystallographic orientation. Previous work has demonstrated the ability of eddy current testing to determine the orientation distribution function (ODF) of a simulated MTR within a simulated microstructure using approximate Bayesian computation techniques. This article will extend these methods to realistic MTR configurations. We review the ODF estimation technique, discuss modifications to the algorithm that are required to apply it to realistic microstructures and then demonstrate its use on simulated eddy current data of a real titanium specimen.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Non-Destructive Testing Techniques · Probabilistic and Robust Engineering Design · Hydrogen embrittlement and corrosion behaviors in metals

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (23)

Show source-provided references
1. A. Pilchak, A. Hutson, J. Porter, D. Buchanan, and R. John, Proceedings of the 13th World Conference on Titanium, In: V. Venkatesh, A. Pilchak, J. Allison, S. Ankem, R. Boyer, J. Christodoulou, H. Fraser, M. Imam, Y. Kosaka, H. Rack, A. Chatterjee, and A. Woodfield (eds.), (Wiley, New Jersey, 2016), p. 993
2. J. Qui, Y. Ma, J. Lei, Y. Liu, A. Huang, D. Rugg, and R. Yang, Metall. Mater. Trans. A 45, 6075 (2014)10.1007/s11661-014-2541-5 ↗
3. L. Toubal, P. Bocher, and A. Moreau, Int. J. Damage Mech. 24, 629 (2015)10.1177/1056789514541823 ↗
4. J. Tucker, M. Groeber, L. Semiatin, and A. Pilchak, Proceedings of the 13th world conference on titanium,In: V. Venkatesh, A. Pilchak, J. Allison, S. Ankem, R. Boyer, J. Christodoulou, H. Fraser, M. Imam, Y. Kosaka, H. Rack, A. Chatterjee, and A. Woodfield (eds.), (Wiley, New Jersey, 2016), p. 1913
5. L. Homa, M. Cherry, and J. Wertz, Inverse Problems 37, 065004 (2021)10.1088/1361-6420/abfb7e ↗
6. V. Randle and O. Engler, Introduction to Texture Analysis: Macrotexture, Microtexture, and Orientation Mapping (Gordon and Breach Science Publishers, Philadelphia, 2000), pp. 13–4110.1201/9781482287479 ↗
7. A. Pilchak, J. Li, and S. Rokhlin, Metall. Mater. Trans. A 44A, 4891 (2013)
8. M. Cherry, S. Sathish, R. Mooers, A. Pilchak, and R. Grandhi, IEEE Trans. Magn. 53(5), 1 (2017)10.1109/TMAG.2017.2650148 ↗
9. C. Balanis, Advanced Engineering Electromagnetics (Wiley, New Jersey, 2012), pp. 311–337
10. B. Auld and J. Moulder, J. Nondestruct. Eval. 18(1), 3 (1999)10.1023/A:1021898520626 ↗
11. M. Suunnaker, A. Busetto, E. Numminen, J. Corander, M. Foll, and C. Dessimoz, PLoS Comput. Biol. 9, e1002803 (2013)10.1371/journal.pcbi.1002803 ↗
12. J. Marin, P. Pudlo, C. Robert, and R. Ryder, Stat. Comput. 22, 1167 (2012)10.1007/s11222-011-9288-2 ↗
13. S.A. Sisson and Y. Fan, Handbook of Markov Chain Monte Carlo, In: S. Brooks, A. Gelman, G.L. Jones, and X. Meng (eds.), (Chapman and Hall/CRC, New York, 2011), p. 313.10.1201/b10905-13 ↗
14. P. Marjoram, J. Molitor, V. Plagnol, and S. Tavare, Proc. Natl. Acad. Sci. 100, 15324 (2003)10.1073/pnas.0306899100 ↗
15. P. Del Moral, A. Doucet, and A. Jasra, Stat. Comput. 22, 1009 (2012)10.1007/s11222-011-9271-y ↗
16. C. Bingham, Ann. Stat. 2, 1201 (1974)10.1214/aos/1176342874 ↗
17. K. Kunze and H. Schaeben, Math. Geol. 8, 917 (2004)10.1023/B:MATG.0000048799.56445.59 ↗
18. H. Schaeben, Textures Microstruct. 13, 51 (1990)10.1155/TSM.13.51 ↗
19. S. Niezgoda and J. Glover, Metall. Mater. Trans. A 44A, 4891 (2013)10.1007/s11661-013-1653-7 ↗
20. A. Arguelles and J. Turner, J. Acoust. Soc. Am. 141, 4347 (2017)10.1121/1.4984290 ↗
21. F. Dong, X. Wang, Q. Yang, H. Liu, D. Xu, Y. Sun, Y. Zhang, R. Xue, and S. Krishnaswamy, Scr. Mater. 154, 40 (2018)10.1016/j.scriptamat.2018.05.009 ↗
22. K. Okazaki and H. Conrad, Trans. Jpn. Inst. Met. 13, 198 (1972)10.2320/matertrans1960.13.198 ↗
23. D. Calvetti and E. Somersalo, Introduction to Bayesian Scientific Computing: ten Lectures on Subjective Computing (Springer, New York, 2007), pp. 144–147